Paul Graham記事 QA システムを OpenAI と Milvus ベクター DB で構築する

上級

これはAI分野の自動化ワークフローで、22個のノードを含みます。主にHtml, Limit, SplitOut, HttpRequest, ManualTriggerなどのノードを使用、AI技術を活用したスマート自動化を実現。 OpenAI と Milvus ベクター データベースを使用して Paul Graham 記事の QA システムを作成

前提条件
  • ターゲットAPIの認証情報が必要な場合あり
  • OpenAI API Key

カテゴリー

ワークフロープレビュー
ノード接続関係を可視化、ズームとパンをサポート
ワークフローをエクスポート
以下のJSON設定をn8nにインポートして、このワークフローを使用できます
{
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      "position": [
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      "parameters": {
        "model": {
          "__rl": true,
          "mode": "list",
          "value": "gpt-4o-mini"
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    },
    {
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      "name": "ワークフロー実行時",
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      "position": [
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      "parameters": {},
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    {
      "id": "c4d4a979-3182-46c9-b145-fa4e6ba57011",
      "name": "記事一覧取得",
      "type": "n8n-nodes-base.httpRequest",
      "position": [
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      ],
      "parameters": {
        "url": "http://www.paulgraham.com/articles.html",
        "options": {}
      },
      "typeVersion": 4.2
    },
    {
      "id": "2e2913f9-d01a-41e8-b1b8-9a981910db7b",
      "name": "記事名抽出",
      "type": "n8n-nodes-base.html",
      "position": [
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      ],
      "parameters": {
        "options": {},
        "operation": "extractHtmlContent",
        "extractionValues": {
          "values": [
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              "key": "essay",
              "attribute": "href",
              "cssSelector": "table table a",
              "returnArray": true,
              "returnValue": "attribute"
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      },
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    {
      "id": "c121dc65-37e3-49d4-b449-f28491e19a6f",
      "name": "項目分割",
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        "options": {},
        "fieldToSplitOut": "essay"
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    {
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      "name": "記事本文取得",
      "type": "n8n-nodes-base.httpRequest",
      "position": [
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      "parameters": {
        "url": "=http://www.paulgraham.com/{{ $json.essay }}",
        "options": {}
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      "typeVersion": 4.2
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    {
      "id": "cd84596e-4046-4d33-9f43-cf464e5c5c01",
      "name": "最初の3件に制限",
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      "position": [
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        "maxItems": 3
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    {
      "id": "318aeeed-fcce-4de2-aa04-92033ef01f28",
      "name": "テキストのみ抽出",
      "type": "n8n-nodes-base.html",
      "position": [
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      "parameters": {
        "options": {},
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          "values": [
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              "cssSelector": "body",
              "skipSelectors": "img,nav"
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      "typeVersion": 1.2
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      "name": "付箋3",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
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      "parameters": {
        "width": 1071.752021563343,
        "height": 285.66037735849045,
        "content": "## Scrape latest Paul Graham essays"
      },
      "typeVersion": 1
    },
    {
      "id": "cf9af24c-9e08-4f27-ad4e-509f72e54a9b",
      "name": "付箋5",
      "type": "n8n-nodes-base.stickyNote",
      "position": [
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      ],
      "parameters": {
        "width": 625,
        "height": 607,
        "content": "## Load into Milvus vector store"
      },
      "typeVersion": 1
    },
    {
      "id": "95e9a59d-1832-4eb7-b58d-ba391c1acb1c",
      "name": "チャットメッセージ受信時",
      "type": "@n8n/n8n-nodes-langchain.chatTrigger",
      "position": [
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      "webhookId": "cd2703a7-f912-46fe-8787-3fb83ea116ab",
      "parameters": {
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      "type": "n8n-nodes-base.stickyNote",
      "position": [
        -380,
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      "parameters": {
        "width": 280,
        "height": 180,
        "content": "## Step 1\n1. Set up a Milvus server based on [this guide](https://milvus.io/docs/install_standalone-docker-compose.md). And then create a collection named `my_collection`.\n2. Click this workflow to load scrape and load Paul Graham essays to Milvus collection.\n"
      },
      "typeVersion": 1
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    {
      "id": "e90a069e-cfd8-49f1-8fe6-a334bb920027",
      "name": "Milvus Vector Store",
      "type": "@n8n/n8n-nodes-langchain.vectorStoreMilvus",
      "position": [
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      "parameters": {
        "mode": "insert",
        "options": {
          "clearCollection": true
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        "milvusCollection": {
          "__rl": true,
          "mode": "list",
          "value": "my_collection",
          "cachedResultName": "my_collection"
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      "id": "d786c471-d564-4f25-beab-f1c7f4559f7a",
      "name": "Default Data Loader",
      "type": "@n8n/n8n-nodes-langchain.documentDefaultDataLoader",
      "position": [
        1460,
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      "parameters": {
        "options": {},
        "jsonData": "={{ $('Extract Text Only').item.json.data }}",
        "jsonMode": "expressionData"
      },
      "typeVersion": 1
    },
    {
      "id": "26730b7b-2bb9-46f8-83c3-3d4ffdfdef57",
      "name": "Embeddings OpenAI",
      "type": "@n8n/n8n-nodes-langchain.embeddingsOpenAi",
      "position": [
        1320,
        240
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      "parameters": {
        "options": {}
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      "id": "de836110-4073-44d5-bbf3-d57f57525f69",
      "name": "Recursive Character Text Splitter",
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      "position": [
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      "parameters": {
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      "parameters": {
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        "height": 120,
        "content": "## Step 2\nChat with this QA Chain with Milvus retriever\n"
      },
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      "position": [
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          "mode": "list",
          "value": "my_collection",
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よくある質問

このワークフローの使い方は?

上記のJSON設定コードをコピーし、n8nインスタンスで新しいワークフローを作成して「JSONからインポート」を選択、設定を貼り付けて認証情報を必要に応じて変更してください。

このワークフローはどんな場面に適していますか?

上級 - 人工知能

有料ですか?

このワークフローは完全無料です。ただし、ワークフローで使用するサードパーティサービス(OpenAI APIなど)は別途料金が発生する場合があります。

ワークフロー情報
難易度
上級
ノード数22
カテゴリー1
ノードタイプ14
難易度説明

上級者向け、16ノード以上の複雑なワークフロー

作成者
Cheney Zhang

Cheney Zhang

@zc277584121

Algorithm engineer at Zilliz, dedicating to the application of vector databases in the AI ecosystem.

外部リンク
n8n.ioで表示

このワークフローを共有

カテゴリー

カテゴリー: 34